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The Research on Hyperspctral Remote Sensing Image Segmentation

Author: ZhuJunLiang
Tutor: TangJuXing;WangMaoZhi
School: Chengdu University of Technology
Course: Computational Mathematics
Keywords: Hyperspectral image segmentation Mathematical Morphology Noise Reduction Dimensionality reduction Mark a watershed
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 117
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Abstract


Hyperspectral remote sensing images with multi-band, high spectral resolution, and the large amount of data to bring the wealth of information, but also brought huge challenges to the data processing. How effective hyperspectral image noise reduction, to reduce the dimension of the hyperspectral image to reduce the amount of data is an important issue in hyperspectral image segmentation. Noise reduction aspects: this article select two conventional frequency domain noise reduction method, high-pass filter and low-pass filtering of hyperspectral image processing. In terms of airspace noise reduction, this paper designed a method based on mathematical morphology, based on the opening and closing operation serial cascade of morphological filters. High pass filtered image to obtain the sharpening, the projecting edge of the image information, while retaining most of the noise of the image information, and the filtering result can be used to dimension reduction operation. Low-pass filtering the image smoothing, reducing image noise. Morphological filter and low-pass filtering effect of the same, also by the image smoothing to reduce image noise, but the smoothing effect is better than the low-pass filtering. Dimensionality reduction: feature extraction to select the two algorithms for processing, the principal component analysis and minimum noise fraction method. The principal component analysis is a powerful tool for multivariate data analysis, widely used in the processing of hyperspectral image dimensionality reduction based on feature extraction, but the method is sensitive to noise, achieve dimensionality reduction process, and also to retain the image noise Information. Based on this proposed minimum noise fraction transform, it is to maximize the signal-to-noise ratio for the linear transformation of the measurement criteria. It in the process of dimension reduction to noise separation. Segmentation: In this paper, marker-based watershed algorithm for hyperspectral image segmentation algorithm for the spindle, and a brief introduction of the basic operations of mathematical morphology pretreatment. With the threshold segmentation and K-means clustering segmentation leads to marked segmentation of the watershed algorithm technology. Dexing Copper Mine hyperspectral remote sensing image data, and compare three segmentation algorithm is given based on the marked superiority of the watershed segmentation algorithm. In this paper, a noise reduction, dimensionality reduction, select the principal component of the feature image segmentation; hyperspectral remote sensing image based on mathematical morphology noise reduction technology; and marked a watershed algorithm is successfully applied to hyperspectral images segmentation.

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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